In processing a digital image, it is common to sharpen the image and enhance fine detail with sharpening algorithms. Typically, sharpening is performed by a convolution process (for example, see A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall: 1989, pp. 249-251). The process of unsharp masking is an example of a convolution-based sharpening process. For example, sharpening an image with unsharp masking can be described by the equation:s(x,y)=i(x,y)**b(x,y)+βf(i(x,y)−i(x,y)**b(x,y))  (0)where:                s(x,y)=output image with enhanced sharpness        i(x,y)=original input image        b(x,y) lowpass filter        β=unsharp mask scale factor        f( )=fringe function        ** denotes two dimensional convolution        (x,y) denotes the xth row and the yth column of an image        
Typically, an unsharp image is generated by convolution of the image with a lowpass filter (i.e., the unsharp image is given by i(x,y)**b(x,y)). Next, the highpass, or fringe data is generated by subtracting the unsharp image from the original image (i.e., the highpass data is found with i(x,y)−i(x,y)**b(x,y)). This highpass data is then modified by either a scale factor β or a fringe function f( ) or both. Finally, the modified highpass data is summed with either the original image or the unsharp image to produce a sharpened image.
A similar sharpening effect can be achieved by modification of the image in the frequency domain (for example, the FFT domain) as is well known in the art of digital signal processing.
It is occasionally desirable to sharpen different regions or pixels of the image by different amounts. For example, is it has been suggested that it is desirable to sharpen the pixels representing human faces to a lesser degree than pixels representing a building. For example, in U.S. Pat. No. 5,682,443 issued Oct. 28, 1997, Gouch et al. describe the modification of the gain of the unsharp mask based on the color of a pixel (and the color of the surrounding neighborhood). A problem with this approach is the undesirable noise enhancement that accompanies the image sharpening.
Alternatively, in U.S. Pat. No. 4,571,635 issued Feb. 18, 1996, Mahmoodi et al. teach a method of deriving an emphasis coefficient β that is used to scale the high frequency information of the digital image depending on the standard deviation of the image pixels within a neighborhood. In addition, in U.S. Pat. No. 5,081,692 issued Jan. 14, 1992, Kwon et al. teach that emphasis coefficient β is based on a center weighted variance calculation. In U.S. Pat. No. 4,761,819 issued Aug. 2, 1988, Denison et al. describe a method where the gain of an unsharp mask is dependent on both a local variance calculation and a noise statistic.
While these methods do indeed sharpen the image while attempting to minimize noise enhancement, they do not vary the sharpening amount based on color, as Gouch describes. It is not apparent how one would go about modifying the gain parameter β of a linear sharpening filter based on both the noise characteristics and the non-noise characteristics (for example color) of the image. Generally, adaptive sharpening methods utilizing noise information apply less sharpening to noisy image areas. However, these image areas may already be receiving a very low amount of sharpening due to other considerations.
In European Patent Application 1174824A2, published Jan. 23, 2002, Gindele and Gallagher describe a noise reduction filter that performs a variable amount of noise reduction based on the color of the pixel. In this filter, the coefficients in the convolution are dynamically derived for each pixel, and depend on the values of the pixel and the neighboring pixels (which are involved in the convolution operation). Because the coefficients of the convolution are dynamically derived for each pixel the filter coefficients are dependent on the pixel values in a local neighborhood. Such a filter is not typically used for image sharpening operations.
Therefore, there exists a need for an improved image sharpening method that adjusts the amount of sharpening based on both the material content of the image and the amount of noise in the image.